At Canonical, we're seeking an experienced Engineering Manager to lead our MLOps & Analytics team. As a key member of our engineering leadership, you'll be responsible for running an effective team, developing colleagues, and ensuring the team's focus, productivity, and unblockage. You will work closely with other engineering managers, product managers, and architects to produce an engineering roadmap with ambitious and achievable goals. Your expertise in popular open-source machine learning tools like Kubeflow, MLFlow, and Feast is crucial, as well as your commitment to healthy engineering practices, documentation, quality, and performance optimisation. This is a globally remote role that requires managing a distributed team of engineers, organizing and leading team processes, conducting one-on-one meetings, identifying and measuring team health indicators, and interacting with a vibrant community. You'll also be responsible for reviewing code produced by other engineers, attending conferences, mentoring and growing direct reports, and working from home with occasional global travel for internal and external events. We're looking for someone with a proven track record of professional experience in software delivery, professional Python development experience, preferably with a track record in open source, and a proven understanding of the machine learning space, its challenges, and opportunities to improve. Experience designing and implementing MLOps solutions is essential, as well as exceptional academic credentials and willingness to travel up to 4 times a year for internal events. Additional skills that may be helpful include hands-on experience with machine learning libraries or tools, building highly automated machine learning solutions for the cloud, experience with container technologies, public clouds, Linux, and open-source software, working knowledge of cloud computing, passion for software quality and testing, and experience working on a distributed team on an open source project.